Input-Aware Flow-Based Computing on Memristor Crossbars With Applications to Edge Detection

Sneak paths in nanoscale memristor crossbars have traditionally been viewed as a problem in the use of memristor crossbars as non-volatile replacements of traditional volatile RAM memories. We show that the sneak paths in a memristor crossbar can be employed to perform computation that exploits device-level parallelism. Our computation can be performed in the memory and does not require data to be moved between a processor and a memory unit - thereby, avoiding the von Neumann bottleneck. We demonstrate the potential of our approach by applying it to a basic problem in computer vision: edge detection in an image. Our results show that the flow-based computing approach on nanoscale memristor crossbars can be used to obtain high-quality approximations of edge detection. We have synthesized multiple 8 × 8 crossbar circuits for this purpose - a single crossbar circuit for detecting edges between all possible pixel pairs with ~85% accuracy, and another family of input-aware crossbars with higher performance over realworld images. The family of input-aware crossbars together performs approximate edge detection for a subset of pixel pairs obtained from analyzing the BSD500 database, and the resultant images are of a quality comparable to exact edge detection.